Model-Based Graphics Recognition

  • Marc Vuilleumier Stückelberg
  • David Doermann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1941)


In this paper, we illustrate the use of a novel probabilistic framework for document analysis on typical problems of document layout analysis and graphics recognition. Our system uses an explicit descriptive model of the document class to find the most likely interpretation of a scanned document image. In contrast to the traditional pipeline architecture, our system carries out all stages of the analysis with a single inference engine, allowing for an end-to-end propagation of the uncertainty.


Document Image Hough Transform Graphic Decomposition Bitmap Image Document Object Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Marc Vuilleumier Stückelberg
    • 1
  • David Doermann
    • 2
  1. 1.CUIUniversity of GenevaGeneva 4Switzerland
  2. 2.LAMPUniversity of MarylandUSA

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